Data visualizations are all around us and our students. And we incorporate data visualizations (e.g., graphs, maps, tables) into our teaching with data. How can we create better visualizations? How do we develop our visualizations to tell a story with our data?

Join us each Wednesday as we dive into the world of data visualization and think about different aspects of creating more effective data visualizations.

This week’s topic is: Challenges and Benefits of Visualizing Data Over Time

Looking at 3 variables at once

I am willing to bet that few would say that the divorce rate in Maine over time is driven by the per capita consumption of margarine, but this great graph by Spurious Correlations certainly leads you to make sense of the data in that way.

And here in lies a fundamental struggle with using two y-axes…how in the world do you interpret the data?

Double y-axes: why the temptation

There are many reasons why scientists, and even your students, would want to make a graph with two y-axes:

you have three distinct variables

you have one independent and potentially two dependent variables

your dependent variables have completely different scales

So, for example you can get a graph like this [let’s ignore for a moment the content of what is being displayed in the graph]. Technically the graph designer here has employed some good techniques to help us understand the graph better. For example, the color of the axes and axis labels aligns with the color of the data points. This helps solve one of the biggest struggles with two y-axes graphs, which data go with which axis/scale?

However, I don’t know about you but I am still left scratching my head to figure out what this graph is trying to convey to me about the data. Is it important that the green is all to one side and is higher than the red and blue? Is it important that the blue goes below the red?

Well, here is lies another common struggle with two y-axes graphs…our eyes are drawn to points where lines and data cross. And before we know it our brain has raced ahead trying to make meaning of that connection/cross point. But where the data cross is actually a reflection of the scales that you chose for your axes not anything relevant to your data (think back to the divorce rates and margarine consumption).

For example, the conception risk around 8 and 17 days of a menstrual cycle has no relevance to being “below” or “above” a level of political conservatism. Bur our brains want to find meaning in that relationship because we are hard wired to think that when things cross it means something.

Our brain wiring makes it hard for us to truly make sense of the data, yet people often think that using two y-axes graphs is a more “slick” or “sophisticated” to show their data.

Food for thought on how to avoid the confusion

What can you do if your students really want to make them? I would suggest asking them questions like:

Why do you want to put two y-axes on your graph?

What are you hoping this graph will communicate about your data?

Why are two y-axes the best way to communicate that?

What do your two y-axes graph not communicate about your data? Or what confusions may be communicated about your data?

Is there another way you could communicate that message about your data?

By asking these questions we start to teach our students how to make their own decisions. Also how what data they have and what they are trying to communicate/answer/explain with their data determines the best choice in graph types.

Rather than just telling them not to make a two y-axes graph, ask them the questions so they decide why not to do it and why to do something else.

Health is something we all care about and all receive data about for ourselves. But how that data is communicated to help people make their decisions and to effect change in the public health fields is sometimes tricky.

This resource can be explored in a classroom from the following angles:

Use their ‘wizard’ tool to think through how what data you have, who you are trying to communicate with, and what you are trying to communicate determines the range of types of data visualizations that will fit your needs

Browse their gallery of the images to see the range of data visualizations that can be used to communicate different aspects of health data

Read the real-world story as a case study to think about how data visualizations could be adapted to help medical professionals work with their patients, and how such communication tools could be incorporated into other fields as well.

Help your students explore the benefits and challenges of communicating health data to consumers through the Visualizing Health resources at http://www.vizhealth.org/!

* It is important to read and understand their data disclaimer “Visualizing Health graphics and data should not be used for real medical decision making. The data used in the graphics are for demonstration purposes only; they are plausible, but may not be medically accurate. If you are concerned about your risk for any of the health conditions included in one or more of the graphics, please talk to your doctor or other health care provider.”

Data visualizations are all around us and our students. And we incorporate data visualizations (e.g., graphs, maps, tables) into our teaching with data. But why do we visualize data in science? How can we create better visualizations? How do we develop our visualizations to tell a story with our data?

Join us each Wednesday as we dive into the world of data visualization and think about different aspects of creating more effective data visualizations.

Predicting the data first

Having students stop and take time to think through what their data will look like – based on their understanding of the system, a conceptual model, a physical model, etc. – helps students explicitly make the connections between what they think broadly will come through in their results and actually what they will see in their data.

Additionally, this also helps the students think through more deeply what the relationships may be in the data and how they could observe those relationships via the data. This sets the students up to be successful in making sense of the data because they have a starting point or an idea of what to look for in the data.

Observing the data

With that idea in their head of what they may find in the data, the students are off to the races to actually look at the data. Taking the time to really look at all of the data, not just individual data points, is easier when they have an idea of what kind of pattern or relationship they expect to see in the data.

Students can successfully find evidence to be able to answer prompts like:

“Explain how the data support or refute your predictions.” rather than “Explain what the data show.”

“Make a claim about what you see in the data with regards to the original question.”

When they have an idea of what to look for, they have a starting place to go from to make sense of the data.

Explaining what they see

The explanation piece gets a lot easier once they have made a prediction and observed the data to see if their prediction of the pattern or relationship in the data is present or absent.

You will find that getting your students to write their Reasoning statements in C-E-R will also come more easily if they are comparing the actual data against their prediction.

Another great benefit of this approach is that students begin to learn more clearly how the reality of the world (through the data) is not as clear, simple, or pretty as they would hope (or as the theory would predict). And this is a great seed to plant in our students in terms of the reality of variation in data and what uncertainty means in science.

What drives patterns in ocean change? – Explore the high variability and movement of surface currents and what that means for the food web around a deep ocean canyon on the West Antarctic Peninsula through Dr. Josh Kohut’s research

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